The automatic detection of chronic pain-related expression: Requirements, challenges and the multimodal EmoPain dataset

Aung, Min Hane, Kaltwang, Sebastian, Romera-Paredes, Bernardino, Martinez, Brais, Singh, Aneesha, Cella, Matteo, Valstar, Michel, Meng, Hongying, Kemp, Andrew, Shafizadeh, Moshen, Elkins, Aaron, Kanakam, Natalie, de Rothschild, Amschel, Tyler, Nick, Watson, Paul, Williams, Amanda C. de C., Pantic, Maja and Bianchi-Berthouze, Nadia (2016) The automatic detection of chronic pain-related expression: Requirements, challenges and the multimodal EmoPain dataset. IEEE Transactions on Affective Computing, 7 (4). pp. 435-451. ISSN 1949-3045

Preview

PDF (Published_Version) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how pain is expressed in chronic pain and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset (named ‘EmoPain’) containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises were considered to reflect traditional scenarios of physiotherapist directed therapy and home-based self-directed therapy. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed.

Item Type:	Article
Uncontrolled Keywords:	chronic low back pain,emotion,pain behaviour,body movement,facial expression,surface electromyography,motion capture,automatic emotion recognition,multimodal database
Depositing User:	LivePure Connector
Date Deposited:	29 Jan 2019 14:30
Last Modified:	21 Jun 2023 09:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/69718
DOI:	10.1109/TAFFC.2015.2462830

Downloads

Actions (login required)

View Item